In [16]:
classes = df.label.unique()
counts = []
for i in classes:
count = len(df[df.label==i])
counts.append(count)
plt.bar(['0', '1','2','3','4','5','6'], counts)
plt.show()
!pip install emot
WARNING: Value for scheme.platlib does not match. Please report this to <https://github.com/pypa/pip/issues/9617> distutils: /usr/local/lib/python3.8/dist-packages sysconfig: /usr/lib/python3.8/site-packages WARNING: Value for scheme.purelib does not match. Please report this to <https://github.com/pypa/pip/issues/9617> distutils: /usr/local/lib/python3.8/dist-packages sysconfig: /usr/lib/python3.8/site-packages WARNING: Value for scheme.headers does not match. Please report this to <https://github.com/pypa/pip/issues/9617> distutils: /usr/local/include/python3.8/UNKNOWN sysconfig: /usr/include/python3.8 WARNING: Value for scheme.scripts does not match. Please report this to <https://github.com/pypa/pip/issues/9617> distutils: /usr/local/bin sysconfig: /usr/bin WARNING: Value for scheme.data does not match. Please report this to <https://github.com/pypa/pip/issues/9617> distutils: /usr/local sysconfig: /usr WARNING: Additional context: user = False home = None root = None prefix = None Defaulting to user installation because normal site-packages is not writeable Collecting emot Downloading emot-3.1-py3-none-any.whl (61 kB) |████████████████████████████████| 61 kB 31 kB/s eta 0:00:011 Installing collected packages: emot WARNING: Value for scheme.headers does not match. Please report this to <https://github.com/pypa/pip/issues/9617> distutils: /home/ubuntu/.local/include/python3.8/UNKNOWN sysconfig: /home/ubuntu/.local/include/python3.8 WARNING: Additional context: user = True home = None root = None prefix = None Successfully installed emot-3.1 WARNING: You are using pip version 21.1; however, version 21.2.4 is available. You should consider upgrading via the '/usr/bin/python -m pip install --upgrade pip' command.
#!pip install tensorflow==2.4.1
#!pip install tensorflow_hub
#!pip install keras tf-models-official pydot graphviz
!pip install transformers
WARNING: Value for scheme.platlib does not match. Please report this to <https://github.com/pypa/pip/issues/9617> distutils: /usr/local/lib/python3.8/dist-packages sysconfig: /usr/lib/python3.8/site-packages WARNING: Value for scheme.purelib does not match. Please report this to <https://github.com/pypa/pip/issues/9617> distutils: /usr/local/lib/python3.8/dist-packages sysconfig: /usr/lib/python3.8/site-packages WARNING: Value for scheme.headers does not match. Please report this to <https://github.com/pypa/pip/issues/9617> distutils: /usr/local/include/python3.8/UNKNOWN sysconfig: /usr/include/python3.8 WARNING: Value for scheme.scripts does not match. Please report this to <https://github.com/pypa/pip/issues/9617> distutils: /usr/local/bin sysconfig: /usr/bin WARNING: Value for scheme.data does not match. Please report this to <https://github.com/pypa/pip/issues/9617> distutils: /usr/local sysconfig: /usr WARNING: Additional context: user = False home = None root = None prefix = None Defaulting to user installation because normal site-packages is not writeable Requirement already satisfied: transformers in /home/ubuntu/.local/lib/python3.8/site-packages (4.9.2) Requirement already satisfied: requests in /usr/lib/python3/dist-packages (from transformers) (2.22.0) Requirement already satisfied: tokenizers<0.11,>=0.10.1 in /home/ubuntu/.local/lib/python3.8/site-packages (from transformers) (0.10.3) Requirement already satisfied: regex!=2019.12.17 in /home/ubuntu/.local/lib/python3.8/site-packages (from transformers) (2021.8.3) Requirement already satisfied: tqdm>=4.27 in /home/ubuntu/.local/lib/python3.8/site-packages (from transformers) (4.62.0) Requirement already satisfied: sacremoses in /home/ubuntu/.local/lib/python3.8/site-packages (from transformers) (0.0.45) Requirement already satisfied: packaging in /usr/local/lib/python3.8/dist-packages (from transformers) (20.9) Requirement already satisfied: filelock in /home/ubuntu/.local/lib/python3.8/site-packages (from transformers) (3.0.12) Requirement already satisfied: numpy>=1.17 in /home/ubuntu/.local/lib/python3.8/site-packages (from transformers) (1.21.1) Requirement already satisfied: huggingface-hub==0.0.12 in /home/ubuntu/.local/lib/python3.8/site-packages (from transformers) (0.0.12) Requirement already satisfied: pyyaml>=5.1 in /usr/lib/python3/dist-packages (from transformers) (5.3.1) Requirement already satisfied: typing-extensions in /home/ubuntu/.local/lib/python3.8/site-packages (from huggingface-hub==0.0.12->transformers) (3.10.0.0) Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.8/dist-packages (from packaging->transformers) (2.4.7) Requirement already satisfied: joblib in /home/ubuntu/.local/lib/python3.8/site-packages (from sacremoses->transformers) (1.0.1) Requirement already satisfied: click in /usr/lib/python3/dist-packages (from sacremoses->transformers) (7.0) Requirement already satisfied: six in /usr/lib/python3/dist-packages (from sacremoses->transformers) (1.14.0) WARNING: Value for scheme.headers does not match. Please report this to <https://github.com/pypa/pip/issues/9617> distutils: /home/ubuntu/.local/include/python3.8/UNKNOWN sysconfig: /home/ubuntu/.local/include/python3.8 WARNING: Additional context: user = True home = None root = None prefix = None WARNING: You are using pip version 21.1; however, version 21.2.4 is available. You should consider upgrading via the '/usr/bin/python -m pip install --upgrade pip' command.
!pip install nltk
WARNING: Value for scheme.platlib does not match. Please report this to <https://github.com/pypa/pip/issues/9617> distutils: /usr/local/lib/python3.8/dist-packages sysconfig: /usr/lib/python3.8/site-packages WARNING: Value for scheme.purelib does not match. Please report this to <https://github.com/pypa/pip/issues/9617> distutils: /usr/local/lib/python3.8/dist-packages sysconfig: /usr/lib/python3.8/site-packages WARNING: Value for scheme.headers does not match. Please report this to <https://github.com/pypa/pip/issues/9617> distutils: /usr/local/include/python3.8/UNKNOWN sysconfig: /usr/include/python3.8 WARNING: Value for scheme.scripts does not match. Please report this to <https://github.com/pypa/pip/issues/9617> distutils: /usr/local/bin sysconfig: /usr/bin WARNING: Value for scheme.data does not match. Please report this to <https://github.com/pypa/pip/issues/9617> distutils: /usr/local sysconfig: /usr WARNING: Additional context: user = False home = None root = None prefix = None Defaulting to user installation because normal site-packages is not writeable Collecting nltk Downloading nltk-3.6.2-py3-none-any.whl (1.5 MB) |████████████████████████████████| 1.5 MB 4.0 MB/s eta 0:00:01 Requirement already satisfied: regex in /home/ubuntu/.local/lib/python3.8/site-packages (from nltk) (2021.8.3) Requirement already satisfied: tqdm in /home/ubuntu/.local/lib/python3.8/site-packages (from nltk) (4.62.0) Requirement already satisfied: joblib in /home/ubuntu/.local/lib/python3.8/site-packages (from nltk) (1.0.1) Requirement already satisfied: click in /usr/lib/python3/dist-packages (from nltk) (7.0) Installing collected packages: nltk WARNING: The script nltk is installed in '/home/ubuntu/.local/bin' which is not on PATH. Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. WARNING: Value for scheme.headers does not match. Please report this to <https://github.com/pypa/pip/issues/9617> distutils: /home/ubuntu/.local/include/python3.8/UNKNOWN sysconfig: /home/ubuntu/.local/include/python3.8 WARNING: Additional context: user = True home = None root = None prefix = None Successfully installed nltk-3.6.2 WARNING: You are using pip version 21.1; however, version 21.2.4 is available. You should consider upgrading via the '/usr/bin/python -m pip install --upgrade pip' command.
import pandas as pd
import numpy as np
import json, re
from tqdm import tqdm_notebook
from uuid import uuid4
import matplotlib.pyplot as plt
## Torch Modules
import torch
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torch.utils.data import Dataset, DataLoader
from emot.emo_unicode import UNICODE_EMOJI , EMOTICONS_EMO
!pip install pytorch-transformers
WARNING: Value for scheme.platlib does not match. Please report this to <https://github.com/pypa/pip/issues/9617> distutils: /usr/local/lib/python3.8/dist-packages sysconfig: /usr/lib/python3.8/site-packages WARNING: Value for scheme.purelib does not match. Please report this to <https://github.com/pypa/pip/issues/9617> distutils: /usr/local/lib/python3.8/dist-packages sysconfig: /usr/lib/python3.8/site-packages WARNING: Value for scheme.headers does not match. Please report this to <https://github.com/pypa/pip/issues/9617> distutils: /usr/local/include/python3.8/UNKNOWN sysconfig: /usr/include/python3.8 WARNING: Value for scheme.scripts does not match. Please report this to <https://github.com/pypa/pip/issues/9617> distutils: /usr/local/bin sysconfig: /usr/bin WARNING: Value for scheme.data does not match. Please report this to <https://github.com/pypa/pip/issues/9617> distutils: /usr/local sysconfig: /usr WARNING: Additional context: user = False home = None root = None prefix = None Defaulting to user installation because normal site-packages is not writeable Requirement already satisfied: pytorch-transformers in /home/ubuntu/.local/lib/python3.8/site-packages (1.2.0) Requirement already satisfied: boto3 in /home/ubuntu/.local/lib/python3.8/site-packages (from pytorch-transformers) (1.18.21) Requirement already satisfied: regex in /home/ubuntu/.local/lib/python3.8/site-packages (from pytorch-transformers) (2021.8.3) Requirement already satisfied: tqdm in /home/ubuntu/.local/lib/python3.8/site-packages (from pytorch-transformers) (4.62.0) Requirement already satisfied: torch>=1.0.0 in /home/ubuntu/.local/lib/python3.8/site-packages (from pytorch-transformers) (1.9.0) Requirement already satisfied: requests in /usr/lib/python3/dist-packages (from pytorch-transformers) (2.22.0) Requirement already satisfied: numpy in /home/ubuntu/.local/lib/python3.8/site-packages (from pytorch-transformers) (1.21.1) Requirement already satisfied: sentencepiece in /home/ubuntu/.local/lib/python3.8/site-packages (from pytorch-transformers) (0.1.96) Requirement already satisfied: sacremoses in /home/ubuntu/.local/lib/python3.8/site-packages (from pytorch-transformers) (0.0.45) Requirement already satisfied: typing-extensions in /home/ubuntu/.local/lib/python3.8/site-packages (from torch>=1.0.0->pytorch-transformers) (3.10.0.0) Requirement already satisfied: s3transfer<0.6.0,>=0.5.0 in /home/ubuntu/.local/lib/python3.8/site-packages (from boto3->pytorch-transformers) (0.5.0) Requirement already satisfied: botocore<1.22.0,>=1.21.21 in /home/ubuntu/.local/lib/python3.8/site-packages (from boto3->pytorch-transformers) (1.21.21) Requirement already satisfied: jmespath<1.0.0,>=0.7.1 in /home/ubuntu/.local/lib/python3.8/site-packages (from boto3->pytorch-transformers) (0.10.0) Requirement already satisfied: urllib3<1.27,>=1.25.4 in /usr/lib/python3/dist-packages (from botocore<1.22.0,>=1.21.21->boto3->pytorch-transformers) (1.25.8) Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /usr/local/lib/python3.8/dist-packages (from botocore<1.22.0,>=1.21.21->boto3->pytorch-transformers) (2.8.1) Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil<3.0.0,>=2.1->botocore<1.22.0,>=1.21.21->boto3->pytorch-transformers) (1.14.0) Requirement already satisfied: joblib in /home/ubuntu/.local/lib/python3.8/site-packages (from sacremoses->pytorch-transformers) (1.0.1) Requirement already satisfied: click in /usr/lib/python3/dist-packages (from sacremoses->pytorch-transformers) (7.0) WARNING: Value for scheme.headers does not match. Please report this to <https://github.com/pypa/pip/issues/9617> distutils: /home/ubuntu/.local/include/python3.8/UNKNOWN sysconfig: /home/ubuntu/.local/include/python3.8 WARNING: Additional context: user = True home = None root = None prefix = None WARNING: You are using pip version 21.1; however, version 21.2.4 is available. You should consider upgrading via the '/usr/bin/python -m pip install --upgrade pip' command.
## PyTorch Transformer
from pytorch_transformers import RobertaModel, RobertaTokenizer
from pytorch_transformers import RobertaForSequenceClassification, RobertaConfig
## Check if Cuda is Available
print(torch.cuda.is_available())
True
#from google.colab import drive
#drive.mount('/content/drive', force_remount=True)
df = pd.read_csv('marinus_trafficking_10k_cleansed.csv',encoding="utf-8")
df.head()
| id | label | title | body | |
|---|---|---|---|---|
| 0 | 2632347 | 4 | Ms Chrissy - 22 | Tall <br> Sexy<br> Slim<br> 100% real<br> Reas... |
| 1 | 11645643 | 1 | Everybody loves a Filipina Girl - 29 | Hi there Boys it's Miss Sweet 1OO% Filipina gi... |
| 2 | 15199346 | 4 | NEW Sexy Asian girl⛔️⛔️⛔️⛔️⛔️SUPER HOOOT⛔️⛔️⛔️... | Hello Gentlemen⚜⚜<br> I am Mimi Asian Girl <br... |
| 3 | 14477454 | 0 | Freaky . TS 🍆🍑 TOP AND BOTTOM (9INCH) fun pack... | • So Treat Yourself to Heaven on Earth...🌍 YOU... |
| 4 | 11266475 | 2 | ❤Kinky Kaylani👑Rear Access &VIP visits👑Great R... | Aren't you tired of the old bait and switch or... |
len(df.body)
12350
# Dropping the 'id' column as it does not add any value
df.drop('id', axis='columns', inplace=True)
df.dropna(inplace=True)
We have two classes in the dataset
df.label.unique()
array([4, 1, 0, 2, 3, 5, 6])
len(df.body)
11850
Let's check how equally distributed those classes are.
classes = df.label.unique()
counts = []
for i in classes:
count = len(df[df.label==i])
counts.append(count)
plt.bar(['0', '1','2','3','4','5','6'], counts)
plt.show()
classes.sort()
from sklearn.utils.class_weight import compute_class_weight
#compute the class weights
class_wts = compute_class_weight('balanced', classes, df.label)
/mnt/disks/user/anaconda3/envs/cuda111/lib/python3.8/site-packages/sklearn/utils/validation.py:70: FutureWarning: Pass classes=[0 1 2 3 4 5 6], y=0 4
1 1
2 4
3 0
4 2
..
12345 5
12346 1
12347 5
12348 5
12349 2
Name: label, Length: 11850, dtype: int64 as keyword args. From version 1.0 (renaming of 0.25) passing these as positional arguments will result in an error
warnings.warn(f"Pass {args_msg} as keyword args. From version "
class_weights_dict = dict(enumerate(class_wts))
class_weights_dict
{0: 0.874409681227863,
1: 0.9205313446749009,
2: 0.5006971732792496,
3: 2.2071149189793258,
4: 0.4976064499874024,
5: 3.7956438180653427,
6: 21.428571428571427}
# demojization
def demojizeText(text):
for emot in UNICODE_EMOJI:
text = text.replace(emot, " " + "_".join(UNICODE_EMOJI[emot].replace(":","").split()) + " ")
#text = text.replace(emot, " " + UNICODE_EMOJI[emot].replace(":","") + " ")
text=text.replace('_'," ")
return text
import re
def cleanhtml(raw_html):
cleanr = re.compile('<.*?>')
cleantext = re.sub(cleanr, '', raw_html)
return cleantext
x = demojizeText('NEW Sexy Asian girl⛔️⛔️⛔️⛔️⛔️SUPER HOOOT⛔️⛔️⛔️...')
x
'NEW Sexy Asian girl no entry ️ no entry ️ no entry ️ no entry ️ no entry ️SUPER HOOOT no entry ️ no entry ️ no entry ️...'
raw_html = '<br>I am an Asian girl.</br>'
clean_text = cleanhtml(raw_html)
clean_text
'I am an Asian girl.'
df.title = df.title.apply(lambda x: cleanhtml(x))
df.title = df.title.apply(lambda x: demojizeText(x))
df.body = df.body.apply(lambda x: cleanhtml(x))
df.body = df.body.apply(lambda x: demojizeText(x))
df.label[0]
4
df.body[0]
"Tall Sexy Slim 100% real Reasonable Clean I'm the girl your looking for and I love to party Also 2 girl deal (ask about it) Serious inquires only <br"
config = RobertaConfig.from_pretrained('roberta-base')
config.num_labels = len(classes)
config
100%|██████████| 481/481 [00:00<00:00, 316117.24B/s]
{
"architectures": [
"RobertaForMaskedLM"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": 0,
"eos_token_id": 2,
"finetuning_task": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_norm_eps": 1e-05,
"max_position_embeddings": 514,
"model_type": "roberta",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"num_labels": 7,
"output_attentions": false,
"output_hidden_states": false,
"pad_token_id": 1,
"pruned_heads": {},
"torchscript": false,
"type_vocab_size": 1,
"vocab_size": 50265
}
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
model = RobertaForSequenceClassification(config)
100%|██████████| 898823/898823 [00:00<00:00, 972189.98B/s] 100%|██████████| 456318/456318 [00:00<00:00, 612947.07B/s]
# feature preparation
def prepare_features(seq_1, max_seq_length = 300,
zero_pad = False, include_CLS_token = True, include_SEP_token = True):
## Tokenzine Input
tokens_a = tokenizer.tokenize(seq_1)
## Truncate
if len(tokens_a) > max_seq_length - 2:
tokens_a = tokens_a[0:(max_seq_length - 2)]
## Initialize Tokens
tokens = []
if include_CLS_token:
tokens.append(tokenizer.cls_token)
## Add Tokens and separators
for token in tokens_a:
tokens.append(token)
if include_SEP_token:
tokens.append(tokenizer.sep_token)
input_ids = tokenizer.convert_tokens_to_ids(tokens)
## Input Mask
input_mask = [1] * len(input_ids)
## Zero-pad sequence lenght
if zero_pad:
while len(input_ids) < max_seq_length:
input_ids.append(0)
input_mask.append(0)
return torch.tensor(input_ids).unsqueeze(0), input_mask
msg = "Hey sexy lady!"
prepare_features(msg)
(tensor([[ 0, 11468, 13350, 6429, 328, 2]]), [1, 1, 1, 1, 1, 1])
class Intents(Dataset):
def __init__(self, dataframe):
self.len = len(dataframe)
self.data = dataframe
def __getitem__(self, index):
utterance = self.data.body[index]
label = self.data.label[index]
X, _ = prepare_features(utterance)
y = self.data.label[index]
return X, y
def __len__(self):
return self.len
df.index
Int64Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8,
9,
...
12340, 12341, 12342, 12343, 12344, 12345, 12346, 12347, 12348,
12349],
dtype='int64', length=11850)
train_dataset.index
RangeIndex(start=0, stop=9480, step=1)
train_size = 0.8
train_dataset=df.sample(frac=train_size,random_state=200).reset_index(drop=True)
test_dataset=df.drop(df.index[train_dataset.index]).reset_index(drop=True)
test_dataset.head()
| label | title | body | |
|---|---|---|---|
| 0 | 1 | (Black Women) - Let me show you something diff... | Hi there gents.. Perhaps you've had a demandi... |
| 1 | 0 | STAG PARTY STRIPPERS $250HR For 2 ExoTic STrip... | CALL TOLL FREE 1 VISIT ANGEL EYEZ 7 WEBSITES ... |
| 2 | 1 | *** Flawless gorgeous beauty who will make you... | () Hi I am Spanish & Indian mixed I’m 21 5'7''... |
| 3 | 2 | Sexy Exotic Latina Playmate.....Waitng for you... | ═ red heart Will Make Your Wildest Dreams Co... |
| 4 | 2 | heart with ribbon Asian Girl Jane crown cr... | Hey guys! My name is Jane. I am well-reviewed ... |
print("FULL Dataset: {}".format(df.shape))
print("TRAIN Dataset: {}".format(train_dataset.shape))
print("TEST Dataset: {}".format(test_dataset.shape))
FULL Dataset: (11848, 3) TRAIN Dataset: (9478, 3) TEST Dataset: (2370, 3)
training_set = Intents(train_dataset)
testing_set = Intents(test_dataset)
training_set[0]
(tensor([[ 0, 80, 7754, 122, 19, 1275, 2549, 80, 7754, 1437,
1437, 7817, 13, 5, 235, 26470, 4128, 116, 38, 437,
110, 6429, 1437, 21041, 1437, 2064, 14189, 2512, 359, 27707,
11, 41, 2770, 676, 1437, 21121, 1144, 1437, 17175, 13,
18025, 22164, 7586, 41926, 38, 581, 989, 47, 6923, 55,
1437, 1649, 2458, 33549, 18537, 9357, 13301, 2458, 1437, 1458,
19020, 14081, 1437, 38384, 359, 23317, 29942, 9567, 47, 240,
1437, 1275, 1144, 33549, 18537, 9357, 603, 1250, 122, 577,
1437, 19953, 1437, 9117, 9, 1085, 53, 4002, 359, 11564,
13301, 2458, 1437, 1437, 440, 614, 1011, 268, 1437, 34588,
27899, 1437, 1437, 3310, 50, 2788, 1437, 1437, 30828, 15099,
129, 1437, 7377, 4797, 1437, 80, 7754, 1437, 1437, 1599,
75, 3844, 127, 86, 7586, 38, 1979, 75, 3844, 14314,
1437, 15686, 1437, 91, 2507, 1437, 14755, 1437, 537, 4202,
5961, 1437, 1437, 28696, 3809, 2]]),
4)
training_set.__getitem__(0)[0].shape
torch.Size([1, 146])
model(training_set.__getitem__(0)[0])
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device
device(type='cuda')
model = model.cuda()
# Parameters
params = {'batch_size': 1,
'shuffle': True,
'drop_last': False,
'num_workers': 1}
training_loader = DataLoader(training_set, **params)
testing_loader = DataLoader(testing_set, **params)
loss_function = nn.CrossEntropyLoss()
learning_rate = 1e-05
optimizer = optim.Adam(params = model.parameters(), lr=learning_rate)
## Test Forward Pass
inp = training_set.__getitem__(0)[0].cuda()
output = model(inp)[0]
print(output.shape)
torch.Size([1, 7])
torch.__version__
'1.9.0+cu102'
for x in enumerate(training_loader):
print(x)
(0, [tensor([[[ 0, 20920, 37196, 2262, 1437, 1437, 38, 524, 9963, 7997,
31, 1817, 1437, 1528, 352, 664, 359, 1256, 328, 7328,
97, 1972, 47, 192, 15, 741, 642, 1437, 47, 40,
28, 22431, 77, 1782, 162, 11, 621, 328, 1437, 370,
40, 28, 1372, 77, 52, 972, 13, 686, 328, 38,
524, 2117, 11261, 1437, 38, 904, 1528, 10483, 359, 372,
138, 328, 1437, 1437, 38, 348, 17627, 17274, 6718, 1437,
3793, 11052, 4122, 3024, 359, 10, 809, 45365, 182, 37480,
328, 1437, 1308, 13076, 32, 195, 108, 306, 1437, 2491,
717, 12, 1978, 12, 2022, 1437, 13955, 29920, 4, 1437,
1437, 19246, 3672, 68, 2466, 19246, 1437, 1437, 1308, 1441,
229, 530, 16, 17175, 12846, 8495, 4250, 328, 5178, 27560,
6034, 787, 218, 8391, 73, 14856, 17030, 12, 25503, 1942,
2822, 221, 2857, 12846, 49703, 428, 2]]]), tensor([4])])
(1, [tensor([[[ 0, 28, 9708, 652, 19, 12382, 2473, 1437, 28, 9708,
652, 19, 12382, 2473, 1437, 381, 6988, 7, 1109, 110,
38689, 13, 17054, 44409, 727, 207, 30323, 221, 2371, 328,
1437, 1437, 28, 9708, 652, 19, 12382, 2473, 1437, 28,
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(1899, [tensor([[[ 0, 978, 4041, 112, 16271, 111, 68, 2466, 3672, 1589,
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(1901, [tensor([[[ 0, 12726, 1437, 18105, 5564, 1437, 32067, 455, 809, 14204,
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(1902, [tensor([[[ 0, 32500, 20857, 33950, 4322, 50, 4516, 25938, 37924, 23177,
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(1903, [tensor([[[ 0, 35331, 6119, 33662, 1666, 63, 310, 86, 4, 17758,
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(1904, [tensor([[[ 0, 12289, 1064, 281, 4356, 8679, 8, 5902, 1256, 157,
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(1905, [tensor([[[ 0, 39574, 38917, 22138, 13350, 21905, 12932, 765, 219, 1227,
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(1906, [tensor([[[ 0, 1308, 766, 16, 1063, 718, 895, 1437, 1197, 1144,
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(1907, [tensor([[[ 0, 21121, 1144, 11420, 208, 3439, 21121, 1144, 7545, 28325,
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(1911, [tensor([[[ 0, 20920, 38, 437, 248, 7138, 1640, 1506, 242, 3226,
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(1912, [tensor([[[ 0, 27233, 11582, 49748, 49748, 49748, 30022, 27233, 11582, 49748,
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(1913, [tensor([[[ 0, 11238, 15446, 5, 8426, 4954, 865, 20081, 12346, 12382,
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(1914, [tensor([[[ 0, 38, 524, 5, 92, 1816, 11, 1139, 19, 23739,
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(1915, [tensor([[[ 0, 15538, 7326, 22563, 1437, 604, 17, 27, 29, 929,
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(1916, [tensor([[[ 0, 7989, 12267, 99, 24, 74, 28, 101, 7, 14235,
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(1919, [tensor([[[ 0, 1308, 597, 3226, 2420, 387, 23082, 4, 175, 1437,
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(1920, [tensor([[[ 0, 7817, 13, 41, 11179, 529, 19, 41, 36670, 847,
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(1921, [tensor([[[ 0, 8814, 33, 103, 1531, 42, 10824, 1437, 603, 12019,
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(1925, [tensor([[[ 0, 7817, 13, 5, 1816, 220, 1883, 28749, 1793, 402,
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(1927, [tensor([[[ 0, 11468, 272, 4189, 1437, 4356, 11502, 1437, 1437, 39787,
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(1928, [tensor([[[ 0, 38, 348, 393, 626, 932, 101, 42, 137, 53,
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(1930, [tensor([[[ 0, 1308, 766, 16, 1275, 4, 1437, 38, 437, 10,
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(1932, [tensor([[[ 0, 20920, 43252, 1308, 10704, 16, 20611, 763, 178, 939,
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(1933, [tensor([[[ 0, 17232, 22629, 4, 38, 437, 17176, 2560, 1437, 13301,
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(1934, [tensor([[[ 0, 2116, 2458, 1437, 1144, 3235, 1437, 2116, 2458, 344,
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(1935, [tensor([[[ 0, 1275, 1144, 1437, 1275, 1144, 1437, 1275, 1144, 1437,
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(1936, [tensor([[[ 0, 41270, 42682, 43476, 578, 3326, 15134, 1862, 5698, 26624,
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(1945, [tensor([[[ 0, 1653, 397, 16, 5, 761, 9, 537, 1816, 47,
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(1946, [tensor([[[ 0, 20479, 652, 19, 12382, 2473, 20920, 7586, 534, 44667,
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(1948, [tensor([[[ 0, 12191, 37196, 2262, 1437, 1437, 38, 524, 7300, 30612,
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(1950, [tensor([[[ 0, 18509, 8295, 36948, 534, 10885, 4, 175, 80, 7754,
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(1951, [tensor([[[ 0, 43252, 3945, 47, 1227, 286, 10, 2497, 86, 116,
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(1952, [tensor([[[ 0, 38, 10145, 10, 2173, 14, 64, 185, 797, 8,
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(1953, [tensor([[[ 0, 11468, 1064, 281, 4, 21438, 45814, 21438, 4, 18522,
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(1954, [tensor([[[ 0, 10145, 70, 110, 8235, 20218, 172, 2968, 329, 619,
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(1955, [tensor([[[ 0, 42699, 27819, 14447, 19291, 246, 4926, 45898, 20920, 1308,
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(1956, [tensor([[[ 0, 2814, 598, 3102, 13324, 359, 13048, 5370, 1580, 1437,
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(1957, [tensor([[[ 0, 5293, 2477, 5, 1053, 12846, 4789, 5, 468, 33014,
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(1958, [tensor([[[ 0, 13675, 1889, 7744, 41201, 35669, 5438, 23412, 10878, 3015,
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(1959, [tensor([[[ 0, 21121, 1144, 15823, 717, 359, 10649, 21260, 41499, 229,
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(1960, [tensor([[[ 0, 38, 524, 10, 13350, 8, 10364, 2182, 12846, 19506,
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(1968, [tensor([[[ 0, 20920, 22164, 4, 1437, 166, 83, 9064, 23905, 8,
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(1969, [tensor([[[ 0, 11468, 2786, 4356, 24967, 13301, 2458, 1437, 234, 42942,
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(1970, [tensor([[[ 0, 21121, 1144, 1596, 3102, 1972, 259, 16506, 2822, 83,
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(1971, [tensor([[[ 0, 909, 3925, 6148, 1437, 1437, 117, 65, 223, 34851,
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(1972, [tensor([[[ 0, 11468, 1669, 1437, 24, 18, 587, 4, 38, 524,
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(1973, [tensor([[[ 0, 999, 33549, 18537, 9357, 5370, 3443, 8, 44289, 2485,
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(1974, [tensor([[[ 0, 39072, 673, 22294, 14780, 48256, 1275, 1144, 1437, 1275,
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(1975, [tensor([[[ 0, 20920, 38, 437, 19825, 1437, 1437, 13301, 2458, 1437,
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(1976, [tensor([[[ 0, 486, 35, 12289, 1437, 37196, 2262, 1437, 28217, 162,
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(1977, [tensor([[[ 0, 17918, 12605, 36575, 15421, 45503, 10263, 22818, 1729, 597,
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(1978, [tensor([[[ 0, 9049, 1634, 1437, 4108, 1144, 1437, 9049, 1634, 1437,
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(1979, [tensor([[[ 0, 12289, 28361, 1437, 38, 524, 274, 25793, 195, 108,
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(1980, [tensor([[[ 0, 20920, 25763, 462, 2262, 1437, 1437, 8814, 30, 7,
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(1981, [tensor([[[ 0, 1275, 7922, 1437, 1275, 7922, 1437, 14333, 15722, 3602,
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(1982, [tensor([[[ 0, 24361, 6085, 2417, 8, 10964, 14638, 4, 32225, 3654,
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(1983, [tensor([[[ 0, 5495, 3765, 31711, 591, 975, 14434, 440, 5370, 3443,
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(1984, [tensor([[[ 0, 13301, 2458, 1437, 35393, 272, 5382, 3850, 12613, 13301,
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(1985, [tensor([[[ 0, 2054, 640, 19798, 4691, 293, 6709, 22410, 4, 605,
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(1986, [tensor([[[ 0, 38, 437, 92, 11, 1139, 8, 38, 236, 7,
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(1987, [tensor([[[ 0, 25277, 1512, 1437, 5853, 12019, 129, 11, 784, 3290,
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(1988, [tensor([[[ 0, 6524, 259, 328, 31164, 652, 1437, 38, 581, 489,
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1635, 7237, 12, 3955, 652, 1437, 652, 14065, 5137, 689,
1437, 1437, 1649, 2458, 6148, 96, 38593, 6006, 1437, 117,
65, 223, 34851, 1437, 1437, 1649, 2458, 6148, 274, 3935,
14849, 73, 10237, 37420, 40911, 9309, 14079, 23098, 27899, 1437,
34588, 27899, 36, 124, 4539, 1965, 7547, 314, 1437, 4761,
3024, 6328, 2441, 13, 1162, 43, 1437, 1649, 2458, 6148,
727, 207, 2444, 41372, 9309, 5382, 947, 4629, 2118, 1640,
2362, 3981, 181, 3181, 43, 1437, 1649, 2458, 6148, 21940,
347, 13812, 18012, 7981, 1437, 1437, 1437, 83, 4458, 33549,
18537, 9357, 604, 17, 27, 29, 929, 1437, 221, 6148,
33549, 18537, 9357, 3672, 29, 73, 37558, 1640, 45801, 577,
71, 7231, 43, 1437, 2116, 2458, 1437, 8228, 34101, 22294,
14780, 468, 6597, 534, 2747, 8748, 32956, 12413, 13181, 1691,
22954, 10463, 73, 49347, 1480, 104, 1437, 1437, 1830, 1028,
19, 27899, 26509, 73, 49347, 706, 725, 4926, 1830, 1028,
19, 27899, 1437, 28696, 3809, 2]]]), tensor([3])])
(1989, [tensor([[[ 0, 12289, 26458, 18458, 35, 1437, 1437, 38, 524, 7569,
1437, 706, 39542, 793, 490, 32945, 1816, 31, 3523, 3532,
1437, 41, 2222, 1816, 1437, 1437, 1437, 24361, 809, 2491,
347, 706, 1718, 19, 6921, 3024, 1437, 83, 13350, 15914,
1437, 19, 10, 1763, 9, 13350, 2473, 350, 4, 1437,
38, 657, 127, 633, 117, 6022, 1437, 1437, 38, 101,
9310, 12606, 42326, 12606, 4082, 46343, 548, 1437, 229, 48900,
354, 48900, 49104, 26058, 163, 428, 48900, 73, 15057, 387,
48900, 49104, 12606, 863, 231, 44259, 44116, 12606, 44259, 466,
178, 901, 1531, 8235, 631, 7586, 1437, 166, 40, 258,
2254, 349, 97, 328, 152, 16, 41, 28922, 12154, 11088,
17182, 17831, 1457, 1931, 25121, 2458, 1437, 1437, 17087, 38831,
2788, 162, 122, 35, 1437, 1437, 44116, 2965, 73, 16320,
1946, 44116, 12606, 1437, 1437, 14159, 35, 2]]]), tensor([4])])
(1990, [tensor([[[ 0, 93, 578, 42699, 9357, 1437, 42699, 9357, 93, 18242,
1437, 1437, 1437, 29293, 5543, 38718, 5543, 38718, 5543, 93,
45610, 29293, 5543, 11869, 5, 8733, 38924, 1258, 29293, 5543,
38718, 5543, 38718, 5543, 93, 45610, 2117, 112, 673, 7,
361, 35, 246, 673, 1589, 262, 360, 10, 186, 1437,
28775, 8210, 2880, 8, 230, 4467, 3102, 813, 29, 28775,
8210, 4556, 38093, 8, 944, 5144, 28775, 8210, 25449, 5,
1079, 28775, 8210, 11869, 110, 308, 849, 134, 93, 578,
1437, 13301, 2458, 1437, 93, 578, 486, 122, 1437, 93,
578, 1437, 13301, 2458, 1437, 93, 578, 1437, 80, 7754,
1437, 370, 6565, 5, 275, 1437, 8, 52, 214, 259,
1437, 80, 7754, 1437, 6252, 8, 1649, 201, 122, 328,
1437, 4954, 865, 1437, 601, 5243, 221, 5101, 9350, 2005,
14290, 5267, 6164, 34030, 28775, 8210, 15041, 5089, 36890, 42699,
9357, 3310, 122, 35, 1437, 1437, 28696, 3809, 2]]]), tensor([4])])
(1991, [tensor([[[ 0, 13301, 2458, 1437, 13301, 2458, 1437, 13301, 2458, 1437,
13301, 2458, 1437, 5178, 12408, 3450, 20944, 328, 36356, 2444,
208, 3439, 328, 1437, 13301, 2458, 1437, 13301, 2458, 1437,
13301, 2458, 1437, 13301, 2458, 1437, 1437, 41270, 2860, 19732,
55, 12058, 2038, 544, 1437, 41270, 3015, 13718, 4086, 11759,
10483, 8, 2254, 84, 8375, 2842, 328, 1437, 41270, 10326,
8, 940, 317, 1437, 2497, 3421, 286, 121, 1437, 1275,
1144, 1437, 1275, 1144, 1437, 52, 581, 4198, 47, 146,
275, 2157, 11, 2579, 86, 4, 3401, 3310, 122, 35,
1437, 1275, 1144, 1437, 3378, 20859, 15581, 31, 305, 10751,
250, 1437, 220, 7, 6036, 1437, 1347, 16287, 35, 1437,
1437, 3378, 20859, 1589, 21502, 24590, 73, 11428, 857, 7861,
1437, 1347, 1869, 4576, 35, 2843, 3170, 27594, 2424, 24590,
3593, 42, 2329, 2]]]), tensor([4])])
(1992, [tensor([[[ 0, 11468, 1669, 328, 38, 216, 2230, 99, 1669, 236,
8, 187, 38, 437, 2530, 38, 437, 45, 2509, 11,
816, 426, 48306, 1437, 38, 437, 10, 2038, 693, 14,
16, 3610, 19, 301, 8, 38, 437, 129, 546, 13,
2099, 1437, 117, 22052, 7391, 1437, 117, 5373, 2682, 328,
38, 129, 1394, 14, 47, 489, 84, 1291, 10, 3556,
328, 1437, 3401, 185, 10, 356, 23, 127, 4392, 18803,
8, 114, 47, 101, 99, 47, 192, 38, 74, 657,
13, 47, 7, 1511, 162, 44660, 1437, 646, 45839, 742,
50141, 12, 50141, 10975, 10555, 7, 283, 81, 742, 50141,
12, 50141, 10975, 11018, 1308, 3424, 742, 50141, 12, 50141,
10975, 16395, 1464, 742, 2]]]), tensor([0])])
(1993, [tensor([[[ 0, 379, 5251, 1812, 734, 541, 5251, 727, 734, 1898,
5251, 10529, 734, 2466, 5251, 3982, 734, 534, 5216, 574,
15, 272, 5216, 574, 16, 389, 5251, 3982, 734, 2466,
5251, 1878, 12389, 14662, 347, 2118, 10463, 10616, 1878, 734,
35507, 259, 2710, 9, 498, 486, 13, 1553, 90, 122,
52, 120, 455, 1769, 2]]]), tensor([4])])
(1994, [tensor([[[ 0, 11468, 1669, 127, 766, 3713, 4, 38, 424, 145,
10, 1099, 1816, 8, 802, 14, 939, 74, 109, 103,
38384, 383, 3422, 7586, 38, 424, 195, 73, 401, 16157,
181, 11622, 38, 33, 2579, 1062, 6181, 19, 10, 28068,
7843, 415, 2553, 14, 38, 2638, 2292, 22697, 7586, 10836,
47, 3679, 162, 27776, 7586, 1640, 38, 64, 28, 1348,
23, 32638, 36, 36205, 43, 130, 411, 292, 65, 4,
38, 904, 786, 6022, 518, 1437, 221, 2857, 32, 2822,
4, 38, 64, 28, 1348, 23, 36, 466, 2890, 43,
130, 130, 707, 19671, 134, 1437, 1437, 2822, 221, 2857,
727, 207, 19020, 14081, 440, 13061, 440, 39148, 3275, 2540,
1437, 5266, 19943, 2540, 727, 207, 6911, 2]]]), tensor([2])])
(1995, [tensor([[[ 0, 10, 1242, 1073, 10505, 4, 175, 36, 15954, 24,
66, 122, 43, 1437, 28122, 8625, 21688, 40375, 22954, 9309,
2747, 382, 1862, 21688, 40375, 221, 12894, 28714, 15, 84,
5178, 20685, 27822, 13732, 22954, 9309, 2747, 30235, 7, 143,
4985, 1437, 16327, 6006, 7561, 16646, 1437, 41910, 565, 6532,
36698, 12471, 4729, 17831, 3779, 14662, 1437, 39853, 256, 38247,
30, 2351, 110, 308, 7127, 1437, 1437, 24975, 22954, 9309,
2747, 30235, 32803, 5089, 112, 22554, 36, 37049, 344, 1889,
3842, 16718, 43, 43796, 1610, 110, 308, 3504, 1437, 3779,
108, 104, 83, 5178, 30235, 10284, 27789, 24394, 12389, 36558,
32555, 16646, 3842, 39853, 256, 38247, 382, 1862, 43216, 40375,
221, 12894, 28714, 10786, 28122, 18990, 83, 20685, 27822, 13732,
22954, 9309, 2747, 4248, 6324, 6006, 1862, 3779, 16506, 1437,
1437, 1437, 121, 39282, 6362, 18342, 1862, 3842, 4516, 44335,
717, 4248, 15231, 3935, 10540, 8105, 29734, 328, 1437, 10,
1242, 1073, 10505, 4, 175, 36, 13724, 201, 62, 122,
43, 1437, 1437, 114, 1717, 240, 244, 172, 1511, 201,
23, 1437, 1437, 50, 1047, 201, 23, 35, 3335, 636,
4, 102, 1242, 1073, 10505, 1039, 14551, 4, 175, 951,
40, 120, 124, 11, 2842, 19, 1717, 25, 1115, 16506,
1437, 10, 1242, 1073, 10505, 4, 175, 36, 11638, 92,
998, 147, 1717, 64, 39853, 256, 38247, 43, 2]]]), tensor([2])])
(1996, [tensor([[[ 0, 38, 216, 2230, 147, 8, 141, 7, 14204, 47,
19, 127, 25645, 295, 5515, 17916, 4, 1336, 7, 1228,
8, 6056, 5224, 110, 4285, 5065, 409, 454, 47, 20147,
4, 10287, 162, 4, 1437, 1437, 16232, 359, 39099, 1173,
35694, 1437, 30379, 4458, 1437, 381, 12179, 636, 840, 8285,
10326, 359, 13793, 6420, 1437, 440, 13061, 45923, 10859, 621,
562, 19732, 1437, 19020, 32695, 421, 359, 14947, 7189, 1994,
12, 2362, 12, 34727, 25684, 1437, 1437, 13540, 37049, 83,
208, 3439, 12606, 13540, 13709, 17710, 35179, 3721, 12606, 1437,
3310, 50, 14159, 16005, 39007, 870, 3166, 36113, 35669, 1437,
12738, 13, 226, 3019, 1437, 1437, 1437, 28696, 3809, 2]]]), tensor([2])])
(1997, [tensor([[[ 0, 5902, 10991, 20157, 1437, 14327, 1144, 1437, 5, 1969,
4069, 9, 579, 6070, 1506, 748, 3181, 225, 359, 1816,
220, 1883, 21041, 1437, 1437, 28775, 8210, 100, 33, 12726,
3024, 1437, 7992, 35841, 1437, 21121, 6219, 2473, 1437, 2721,
14638, 1437, 11962, 21565, 1437, 21462, 359, 41, 2770, 5350,
11454, 1955, 4, 1437, 28775, 8210, 37478, 34014, 359, 1522,
9049, 1634, 479, 440, 4149, 328, 1437, 38, 31124, 1437,
80, 7754, 1437, 653, 38, 1832, 359, 47, 40, 350,
328, 25503, 10247, 47, 581, 28, 567, 124, 13301, 2458,
1437, 25503, 10247, 598, 15084, 2486, 40210, 25589, 154, 286,
901, 1437, 14711, 13868, 38041, 1437, 14711, 13868, 38041, 1437,
440, 248, 17387, 3421, 1437, 292, 1021, 17, 27, 17036,
1437, 1437, 17175, 5053, 958, 328, 2548, 438, 12019, 4041,
328, 3401, 1198, 12, 6298, 11076, 41, 1946, 11, 3316,
328, 1437, 7377, 4797, 12192, 162, 10, 486, 4832, 2]]]), tensor([2])])
(1998, [tensor([[[ 0, 1596, 55, 360, 11, 407, 15117, 1437, 602, 11076,
13, 294, 8, 307, 129, 4, 1437, 1437, 14159, 162,
129, 35, 479, 4645, 7233, 19432, 1577, 4325, 4325, 139,
2]]]), tensor([1])])
(1999, [tensor([[[ 0, 4536, 12, 560, 12, 28542, 1437, 1437, 50117, 50117,
5632, 944, 196, 289, 5229, 918, 328, 1437, 50117, 50117,
1437, 50117, 50117, 1437, 50117, 50117, 38576, 7, 7359, 19,
1437, 1437, 50117, 50117, 22313, 847, 918, 116, 2381, 1437,
1437, 50117, 50117, 16767, 12, 560, 12, 16767, 8, 1067,
59, 932, 4, 947, 282, 39596, 131, 1832, 10, 18105,
5564, 9572, 29993, 50, 311, 160, 110, 2099, 9492, 4,
1437, 50117, 50117, 13449, 3001, 256, 10388, 2492, 387, 18990,
6019, 16646, 328, 1437, 1437, 50117, 50117, 4528, 32, 46216,
1029, 12080, 101, 7, 120, 38384, 15, 11021, 4, 2381,
1437, 1437, 50117, 50117, 16905, 480, 1702, 110, 3418, 990,
8235, 4, 12738, 69, 932, 4, 1437, 50117, 50117, 1437,
50117, 50117, 1437, 50117, 50117, 27291, 10652, 25838, 3842, 7777,
33714, 1691, 5198, 328, 1437, 50117, 50117, 1437, 50117, 50117,
16549, 5338, 20013, 19, 6003, 7707, 328, 50117, 50117, 50117,
1437, 50117, 50117, 50117, 50117, 1437, 50117, 50117, 50117, 1437,
50117, 50117, 1437, 50117, 50117, 1437, 50117, 50117, 1437, 50117,
50117, 11368, 111, 1437, 50117, 50117, 1437, 50117, 50117, 448,
3063, 597, 111, 1437, 50117, 50117, 1437, 50117, 50117, 36496,
111, 1437, 50117, 50117, 1437, 50117, 50117, 7405, 771, 111,
1437, 50117, 50117, 1437, 50117, 50117, 2515, 119, 4575, 111,
1437, 50117, 50117, 1437, 50117, 50117, 597, 594, 1173, 111,
1437, 50117, 50117, 1437, 50117, 50117, 41933, 1243, 1437, 50117,
50117, 1437, 50117, 50117, 1437, 1437, 1437, 1437, 50117, 50117,
1437, 50117, 50117, 1437, 1437, 1437, 1437, 50117, 50117, 1437,
50117, 50117, 1437, 1437, 1437, 1437, 50117, 1437, 50117, 1437,
50117, 50117, 1437, 50117, 50117, 574, 23775, 1862, 5089, 41390,
31076, 1862, 17678, 3388, 116, 1437, 50117, 50117, 170, 2615,
14943, 370, 3105, 328, 1437, 50117, 50117, 7608, 116, 3047,
21857, 630, 75, 173, 13, 3370, 4, 166, 33, 2]]]), tensor([1])])
(2000, [tensor([[[ 0, 20920, 43252, 127, 766, 16, 10738, 324, 4, 38,
524, 92, 7, 5, 443, 546, 13, 2382, 30228, 22563,
22164, 7, 1930, 103, 1318, 86, 19, 4, 38, 524,
10, 182, 2038, 2382, 30228, 22563, 4002, 14, 64, 14235,
110, 358, 1072, 8, 782, 4, 211, 2533, 2649, 66,
4, 3310, 162, 36891, 3226, 24334, 3226, 1570, 3546, 440,
488, 2251, 28408, 100, 18024, 22954, 10463, 35669, 2]]]), tensor([1])])
(2001, [tensor([[[ 0, 166, 32, 1818, 14705, 8, 8571, 7, 173, 31,
184, 25, 41348, 3092, 1437, 447, 110, 308, 3078, 8,
146, 10, 4114, 1425, 150, 608, 24, 4, 166, 904,
5, 1609, 2746, 2888, 19039, 1795, 9, 1425, 4, 1541,
41348, 3092, 173, 15, 65, 9, 5, 1609, 1703, 4194,
41348, 4836, 4, 590, 84, 4206, 2111, 544, 8, 5192,
323, 467, 1437, 52, 40, 28, 13390, 47, 349, 1149,
9, 5, 169, 7, 1306, 14, 47, 4073, 5, 144,
418, 678, 4, 166, 694, 3092, 7163, 4965, 1437, 14634,
1616, 1437, 8, 4206, 582, 4, 31043, 9443, 49138, 102,
2]]]), tensor([0])])
(2002, [tensor([[[ 0, 318, 47, 214, 546, 13, 5, 23435, 116, 4,
1437, 356, 117, 617, 17220, 370, 303, 12341, 328, 1437,
6420, 1437, 26081, 62, 1437, 4761, 12, 6991, 3024, 6328,
1437, 1437, 8814, 676, 41, 2770, 1852, 19, 162, 328,
1437, 1437, 653, 47, 192, 16, 99, 47, 120, 4,
38, 524, 8112, 771, 1437, 9316, 2544, 154, 652, 19,
15686, 1437, 652, 13897, 10, 13301, 1437, 112, 9332, 207,
3015, 13510, 4281, 1840, 13365, 104, 29, 29942, 230, 17042,
7720, 1437, 1275, 1144, 33549, 18537, 9357, 6420, 1437, 418,
19, 11954, 1437, 1437, 13350, 7992, 6921, 28218, 5856, 17220,
1437, 1510, 7485, 727, 1368, 5039, 12641, 5039, 39099, 1173,
35694, 20560, 652, 1437, 25054, 14638, 19, 5, 4045, 990,
6675, 7, 914, 328, 38, 437, 1227, 77, 47, 32,
328, 1437, 318, 47, 24980, 565, 33, 110, 308, 317,
2540, 109, 8127, 2788, 50, 486, 162, 4, 1437, 1437,
2829, 12382, 652, 1437, 1437, 230, 17042, 7720, 1437, 1437,
31164, 652, 1437, 418, 19, 11954, 1437, 418, 3298, 2]]]), tensor([2])])
(2003, [tensor([[[ 0, 20920, 4, 37183, 5872, 196, 328, 33659, 7633, 34876,
546, 7, 972, 92, 964, 328, 1582, 30228, 1437, 1437,
4045, 1437, 13350, 8, 3739, 9, 1531, 4, 38, 437,
10, 6764, 1640, 70, 5856, 43, 247, 1816, 7290, 34876,
4, 38, 1017, 657, 7, 972, 8, 5393, 110, 38626,
35591, 4, 221, 2857, 32, 588, 8, 595, 4, 1437,
35415, 110, 449, 4291, 116, 39099, 1173, 5192, 2849, 41687,
4, 2290, 6362, 11, 5, 20632, 412, 443, 42, 1559,
4, 38, 189, 28, 7290, 34876, 110, 443, 25, 157,
4, 3310, 50, 2788, 1437, 7, 1040, 103, 86, 4,
5773, 228, 1946, 9680, 42339, 10529, 1209, 846, 4, 15421,
45503, 10263, 22294, 2747, 11088, 39857, 905, 18, 33, 103,
1531, 4, 2]]]), tensor([0])])
(2004, [tensor([[[ 0, 4449, 359, 2253, 1242, 131, 47193, 1437, 1437, 50117,
50117, 36929, 947, 2253, 1242, 131, 6853, 4755, 1437, 50117,
50117, 1779, 370, 214, 19325, 1437, 1437, 50117, 50117, 13984,
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50117, 41417, 3130, 328, 1437, 50117, 50117, 1437, 50117, 50117,
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50117, 1437, 50117, 50117, 50117, 50117, 9685, 44177, 3995, 1437,
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10287, 36, 18076, 1437, 1437, 50117, 50117, 50117, 50117, 50117,
7907, 43, 1437, 50117, 50117, 50117, 50117, 50117, 9685, 44177,
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1437, 50117, 50117, 50117, 50117, 7608, 3300, 66, 37568, 6332,
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116, 1801, 492, 110, 1046, 8, 2259, 8, 20021, 16109,
1437, 1437, 50117, 50117, 50117, 50117, 642, 2857, 4, 152,
16, 129, 13, 167, 1818, 1437, 1437, 50117, 50117, 50117,
50117, 16063, 927, 9337, 4489, 13, 9881, 2099, 4, 1437,
50117, 50117, 50117, 50117, 1437, 50117, 50117, 50117, 50117, 1437,
50117, 50117, 50117, 50117, 19814, 10287, 3130, 36, 9685, 27829,
3130, 12900, 45491, 29, 43, 1437, 50117, 50117, 50117, 50117,
1437, 50117, 50117, 50117, 50117, 1437, 50117, 50117, 50117, 50117,
17762, 10287, 3130, 36, 246, 3130, 12900, 45491, 29, 43,
1437, 50117, 50117, 50117, 50117, 1437, 50117, 50117, 50117, 2]]]), tensor([0])])
(2005, [tensor([[[ 0, 43662, 9253, 47188, 46056, 1437, 35555, 36765, 22063, 10674,
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(2006, [tensor([[[ 0, 38, 9603, 47, 7, 1962, 162, 11, 5, 144,
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(2007, [tensor([[[ 0, 20920, 22164, 939, 424, 92, 11, 1139, 39662, 652,
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(2008, [tensor([[[ 0, 12289, 1437, 38, 524, 854, 14676, 733, 1423, 4926,
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(2009, [tensor([[[ 0, 12289, 8732, 328, 38, 524, 5388, 324, 31, 2499,
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(2010, [tensor([[[ 0, 38, 437, 10, 13350, 24332, 19, 3793, 23739, 11,
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(2011, [tensor([[[ 0, 3045, 5605, 1437, 1437, 12382, 652, 19, 1368, 7747,
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(2012, [tensor([[[ 0, 37514, 114, 52, 17, 27, 241, 10756, 1437, 283,
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(2013, [tensor([[[ 0, 8814, 27707, 11, 402, 4045, 8, 32927, 734, 10,
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(2014, [tensor([[[ 0, 11468, 328, 188, 469, 3102, 11274, 2723, 3131, 1398,
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(2015, [tensor([[[ 0, 12289, 1669, 38, 437, 3992, 12, 3743, 8, 38,
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(2016, [tensor([[[ 0, 30379, 4458, 1437, 30379, 4458, 24980, 565, 26817, 6433,
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4394, 15264, 6362, 2604, 7942, 18760, 46859, 27785, 1437, 1437,
1437, 1197, 1144, 1437, 9049, 1634, 1437, 9049, 1634, 1437,
9049, 1634, 4832, 166, 8012, 598, 30494, 370, 2822, 9561,
4832, 9049, 1634, 1437, 9049, 1634, 1437, 9049, 1634, 1437,
1197, 1144, 1437, 1437, 1437, 1437, 1437, 28696, 3809, 2]]]), tensor([4])])
(9474, [tensor([[[ 0, 4729, 21600, 43400, 2118, 39922, 83, 9788, 3063, 13732,
6015, 12866, 6006, 36558, 35, 1437, 650, 2440, 11720, 158,
5216, 32030, 11595, 32525, 2808, 3764, 1723, 21277, 34128, 359,
20857, 9672, 1437, 650, 2440, 11720, 234, 791, 10089, 30415,
3603, 39790, 1437, 650, 2440, 11720, 10033, 3850, 18453, 1437,
650, 2440, 11720, 305, 20106, 4186, 12154, 208, 12147, 11654,
1437, 650, 2440, 11720, 30334, 717, 18453, 1437, 650, 2440,
11720, 38, 657, 132, 1643, 1437, 650, 2440, 11720, 30392,
17868, 975, 1437, 256, 25938, 3808, 3850, 30392, 8105, 6532,
25133, 487, 26686, 21955, 1437, 1437, 6317, 332, 7606, 22818,
1729, 597, 43953, 8228, 12413, 13181, 1691, 22954, 10463, 1437,
1437, 18640, 1264, 1437, 37881, 29604, 158, 3796, 226, 11674,
234, 6343, 1437, 727, 207, 39932, 2822, 18098, 34628, 1437,
1437, 2548, 438, 12019, 359, 603, 12019, 2]]]), tensor([2])])
(9475, [tensor([[[ 0, 13301, 2458, 1437, 15686, 1437, 1458, 1437, 13301, 1437,
14711, 13868, 38041, 83, 9788, 3063, 13732, 14279, 14711, 13868,
38041, 1437, 13301, 1437, 1458, 1437, 15686, 1437, 1437, 1437,
7377, 4797, 1437, 7377, 4797, 1437, 7377, 4797, 1437, 1437,
1437, 7377, 4797, 1437, 7377, 4797, 1437, 7377, 4797, 1437,
1437, 1437, 11971, 1021, 17, 27, 17036, 1437, 11971, 1021,
17, 27, 17036, 1437, 11971, 1021, 17, 27, 17036, 1437,
4079, 31765, 3672, 29, 11971, 1021, 17, 27, 17036, 1437,
11971, 1021, 17, 27, 17036, 1437, 11971, 1021, 17, 27,
17036, 479, 1437, 1437, 111, 21231, 15099, 129, 111, 9986,
1437, 1437, 249, 1036, 117, 488, 1437, 9986, 6404, 1437,
1437, 9986, 741, 29, 1437, 1437, 9986, 426, 12457, 21025,
110, 1437, 1946, 19487, 45, 626, 1437, 25837, 5435, 1437,
3229, 1437, 25645, 14711, 13868, 38041, 1437, 14711, 13868, 38041,
1437, 14711, 13868, 38041, 2]]]), tensor([3])])
(9476, [tensor([[[ 0, 3201, 7204, 25889, 38, 27789, 6362, 2796, 255, 20245,
38, 3326, 36392, 14795, 10259, 39419, 7586, 10370, 717, 359,
16852, 3721, 1941, 256, 3765, 5382, 1437, 41582, 19, 2422,
31109, 809, 166, 248, 32555, 313, 2518, 131, 29, 3366,
3102, 1816, 14333, 15722, 15113, 12686, 41270, 113, 15417, 196,
1173, 5370, 1580, 1437, 13496, 29689, 5370, 1580, 14333, 15722,
15113, 12686, 41270, 113, 46633, 208, 25729, 14333, 15722, 15113,
12686, 41270, 113, 245, 12, 4239, 14360, 113, 14333, 15722,
15113, 12686, 41270, 19183, 13048, 20978, 5370, 1580, 1437, 16287,
35, 6560, 10503, 312, 1437, 25525, 2376, 1437, 5300, 158,
31998, 1437, 1437, 7377, 1437, 1555, 1174, 49703, 428, 2]]]), tensor([5])])
(9477, [tensor([[[ 0, 603, 1250, 181, 5781, 1250, 658, 2548, 16395, 818,
4558, 1437, 5080, 3760, 71, 365, 1685, 30828, 15099, 129,
1437, 1437, 1878, 73, 5039, 9680, 73, 36646, 2548, 16395,
2704, 158, 1437, 28696, 3809, 2]]]), tensor([2])])
max_epochs = 3
model = model.train()
for epoch in tqdm_notebook(range(max_epochs)):
print("EPOCH -- {}".format(epoch))
for i, (sent, label) in enumerate(training_loader):
optimizer.zero_grad()
sent = sent.squeeze(0)
if torch.cuda.is_available():
sent = sent.cuda()
label = label.cuda()
output = model.forward(sent)[0]
_, predicted = torch.max(output, 1)
loss = loss_function(output, label)
loss.backward()
optimizer.step()
if i%100 == 0:
correct = 0
total = 0
for sent, label in testing_loader:
sent = sent.squeeze(0)
if torch.cuda.is_available():
sent = sent.cuda()
label = label.cuda()
output = model.forward(sent)[0]
_, predicted = torch.max(output.data, 1)
total += label.size(0)
correct += (predicted.cpu() == label.cpu()).sum()
accuracy = 100.00 * correct.numpy() / total
print('Iteration: {}. Loss: {}. Accuracy: {}%'.format(i, loss.item(), accuracy))
<ipython-input-102-dfa1cee8c594>:3: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0 Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook` for epoch in tqdm_notebook(range(max_epochs)):
EPOCH -- 0 Iteration: 0. Loss: 2.115553140640259. Accuracy: 23.333333333333332% Iteration: 100. Loss: 1.6021668910980225. Accuracy: 22.573839662447256% Iteration: 200. Loss: 2.543870449066162. Accuracy: 28.438818565400844% Iteration: 300. Loss: 1.2986783981323242. Accuracy: 24.135021097046412% Iteration: 400. Loss: 2.3568272590637207. Accuracy: 28.438818565400844% Iteration: 500. Loss: 1.6888062953948975. Accuracy: 23.08016877637131% Iteration: 600. Loss: 1.3925234079360962. Accuracy: 23.58649789029536% Iteration: 700. Loss: 1.5061269998550415. Accuracy: 21.814345991561183% Iteration: 800. Loss: 1.3390448093414307. Accuracy: 25.10548523206751% Iteration: 900. Loss: 1.2883663177490234. Accuracy: 25.485232067510548% Iteration: 1000. Loss: 1.1172590255737305. Accuracy: 16.455696202531644% Iteration: 1100. Loss: 1.4301819801330566. Accuracy: 27.932489451476794% Iteration: 1200. Loss: 0.8989196419715881. Accuracy: 25.654008438818565% Iteration: 1300. Loss: 0.8903651237487793. Accuracy: 23.248945147679326% Iteration: 1400. Loss: 1.9856103658676147. Accuracy: 25.569620253164558% Iteration: 1500. Loss: 2.082176685333252. Accuracy: 23.333333333333332% Iteration: 1600. Loss: 1.2473968267440796. Accuracy: 23.628691983122362% Iteration: 1700. Loss: 0.9675955772399902. Accuracy: 26.835443037974684% Iteration: 1800. Loss: 1.211807131767273. Accuracy: 26.62447257383966% Iteration: 1900. Loss: 1.6303064823150635. Accuracy: 23.628691983122362% Iteration: 2000. Loss: 1.738638997077942. Accuracy: 26.793248945147678% Iteration: 2100. Loss: 1.046872615814209. Accuracy: 27.763713080168777% Iteration: 2200. Loss: 1.3706772327423096. Accuracy: 28.649789029535864% Iteration: 2300. Loss: 2.710078716278076. Accuracy: 24.135021097046412% Iteration: 2400. Loss: 1.259486198425293. Accuracy: 27.72151898734177% Iteration: 2500. Loss: 2.5141258239746094. Accuracy: 25.358649789029535% Iteration: 2600. Loss: 2.0318357944488525. Accuracy: 23.206751054852322% Iteration: 2700. Loss: 3.711979627609253. Accuracy: 23.839662447257385% Iteration: 2800. Loss: 1.4403636455535889. Accuracy: 27.763713080168777% Iteration: 2900. Loss: 1.4283422231674194. Accuracy: 24.261603375527425% Iteration: 3000. Loss: 1.518009901046753. Accuracy: 24.641350210970465% Iteration: 3100. Loss: 1.2084449529647827. Accuracy: 28.354430379746834% Iteration: 3200. Loss: 1.9931176900863647. Accuracy: 28.776371308016877% Iteration: 3300. Loss: 1.1081488132476807. Accuracy: 28.059071729957807% Iteration: 3400. Loss: 1.3454538583755493. Accuracy: 18.438818565400844% Iteration: 3500. Loss: 2.659503936767578. Accuracy: 23.88185654008439% Iteration: 3600. Loss: 0.9073798656463623. Accuracy: 29.11392405063291% Iteration: 3700. Loss: 1.2323870658874512. Accuracy: 28.31223628691983% Iteration: 3800. Loss: 1.2575843334197998. Accuracy: 23.58649789029536% Iteration: 3900. Loss: 1.719938039779663. Accuracy: 25.738396624472575% Iteration: 4000. Loss: 1.5173990726470947. Accuracy: 26.62447257383966% Iteration: 4100. Loss: 2.21273136138916. Accuracy: 24.008438818565402% Iteration: 4200. Loss: 2.8410942554473877. Accuracy: 23.29113924050633% Iteration: 4300. Loss: 1.4080934524536133. Accuracy: 30.253164556962027% Iteration: 4400. Loss: 1.101949691772461. Accuracy: 25.23206751054852% Iteration: 4500. Loss: 1.1390697956085205. Accuracy: 26.286919831223628% Iteration: 4600. Loss: 1.3919340372085571. Accuracy: 30.168776371308017% Iteration: 4700. Loss: 2.2718048095703125. Accuracy: 23.037974683544302% Iteration: 4800. Loss: 1.1983327865600586. Accuracy: 25.654008438818565% Iteration: 4900. Loss: 0.6955663561820984. Accuracy: 31.68776371308017% Iteration: 5000. Loss: 1.1855345964431763. Accuracy: 16.11814345991561% Iteration: 5100. Loss: 1.4273535013198853. Accuracy: 30.126582278481013% Iteration: 5200. Loss: 1.0160033702850342. Accuracy: 28.69198312236287% Iteration: 5300. Loss: 1.0446174144744873. Accuracy: 27.046413502109704% Iteration: 5400. Loss: 0.9828331470489502. Accuracy: 26.286919831223628% Iteration: 5500. Loss: 1.8659112453460693. Accuracy: 31.09704641350211% Iteration: 5600. Loss: 3.198134660720825. Accuracy: 28.31223628691983% Iteration: 5700. Loss: 1.2323170900344849. Accuracy: 23.544303797468356% Iteration: 5800. Loss: 5.74146842956543. Accuracy: 27.257383966244724% Iteration: 5900. Loss: 2.6070690155029297. Accuracy: 26.751054852320674% Iteration: 6000. Loss: 2.161986827850342. Accuracy: 27.21518987341772% Iteration: 6100. Loss: 2.781747341156006. Accuracy: 25.864978902953588% Iteration: 6200. Loss: 0.9082421064376831. Accuracy: 27.72151898734177% Iteration: 6300. Loss: 1.6597375869750977. Accuracy: 21.68776371308017% Iteration: 6400. Loss: 1.9740736484527588. Accuracy: 28.69198312236287% Iteration: 6500. Loss: 1.619444727897644. Accuracy: 27.29957805907173% Iteration: 6600. Loss: 1.875312328338623. Accuracy: 25.61181434599156% Iteration: 6700. Loss: 1.0726491212844849. Accuracy: 29.91561181434599% Iteration: 6800. Loss: 0.8556938171386719. Accuracy: 28.0168776371308% Iteration: 6900. Loss: 2.07732892036438. Accuracy: 24.219409282700422% Iteration: 7000. Loss: 2.399209976196289. Accuracy: 26.582278481012658% Iteration: 7100. Loss: 1.4638193845748901. Accuracy: 26.793248945147678% Iteration: 7200. Loss: 0.9419317245483398. Accuracy: 25.485232067510548% Iteration: 7300. Loss: 0.6821495890617371. Accuracy: 27.426160337552744% Iteration: 7400. Loss: 1.0641930103302002. Accuracy: 26.70886075949367% Iteration: 7500. Loss: 1.466426134109497. Accuracy: 11.518987341772151% Iteration: 7600. Loss: 1.6893830299377441. Accuracy: 32.61603375527426% Iteration: 7700. Loss: 2.7957663536071777. Accuracy: 27.21518987341772% Iteration: 7800. Loss: 1.9466824531555176. Accuracy: 30.08438818565401% Iteration: 7900. Loss: 3.614008903503418. Accuracy: 28.649789029535864% Iteration: 8000. Loss: 2.918184995651245. Accuracy: 26.033755274261605% Iteration: 8100. Loss: 0.7799357771873474. Accuracy: 32.19409282700422% Iteration: 8200. Loss: 5.434780120849609. Accuracy: 31.39240506329114% Iteration: 8300. Loss: 0.8438319563865662. Accuracy: 28.227848101265824% Iteration: 8400. Loss: 3.4425694942474365. Accuracy: 34.177215189873415% Iteration: 8500. Loss: 1.4693608283996582. Accuracy: 25.189873417721518% Iteration: 8600. Loss: 1.1504344940185547. Accuracy: 27.51054852320675% Iteration: 8700. Loss: 1.541670560836792. Accuracy: 31.814345991561183% Iteration: 8800. Loss: 1.1164846420288086. Accuracy: 33.67088607594937% Iteration: 8900. Loss: 2.5915379524230957. Accuracy: 32.447257383966246% Iteration: 9000. Loss: 1.0093191862106323. Accuracy: 33.71308016877637% Iteration: 9100. Loss: 1.2331323623657227. Accuracy: 32.95358649789029% Iteration: 9200. Loss: 1.1983927488327026. Accuracy: 33.71308016877637% Iteration: 9300. Loss: 1.289771318435669. Accuracy: 29.282700421940927% Iteration: 9400. Loss: 1.1783026456832886. Accuracy: 32.91139240506329% EPOCH -- 1 Iteration: 0. Loss: 0.8171215653419495. Accuracy: 34.050632911392405% Iteration: 100. Loss: 1.2561485767364502. Accuracy: 33.79746835443038% Iteration: 200. Loss: 0.8310449123382568. Accuracy: 30.59071729957806% Iteration: 300. Loss: 0.8723323345184326. Accuracy: 29.451476793248943% Iteration: 400. Loss: 0.1489783525466919. Accuracy: 29.029535864978904% Iteration: 500. Loss: 1.5403724908828735. Accuracy: 29.831223628691983% Iteration: 600. Loss: 1.217682957649231. Accuracy: 33.12236286919831% Iteration: 700. Loss: 2.6839213371276855. Accuracy: 34.59915611814346% Iteration: 800. Loss: 2.708662748336792. Accuracy: 31.139240506329113% Iteration: 900. Loss: 0.3795117139816284. Accuracy: 33.164556962025316% Iteration: 1000. Loss: 2.2478995323181152. Accuracy: 28.185654008438817% Iteration: 1100. Loss: 0.6784570813179016. Accuracy: 33.79746835443038% Iteration: 1200. Loss: 1.8677377700805664. Accuracy: 34.81012658227848% Iteration: 1300. Loss: 0.9619919061660767. Accuracy: 35.61181434599156% Iteration: 1400. Loss: 1.1067724227905273. Accuracy: 30.337552742616033% Iteration: 1500. Loss: 1.5163342952728271. Accuracy: 33.628691983122366% Iteration: 1600. Loss: 0.49367421865463257. Accuracy: 30.08438818565401% Iteration: 1700. Loss: 0.9893249869346619. Accuracy: 36.413502109704645% Iteration: 1800. Loss: 0.36675241589546204. Accuracy: 33.881856540084385% Iteration: 1900. Loss: 1.715155839920044. Accuracy: 29.873417721518987% Iteration: 2000. Loss: 1.5515165328979492. Accuracy: 35.69620253164557% Iteration: 2100. Loss: 0.5847398638725281. Accuracy: 35.18987341772152% Iteration: 2200. Loss: 1.3075494766235352. Accuracy: 34.89451476793249% Iteration: 2300. Loss: 2.693016290664673. Accuracy: 32.82700421940928% Iteration: 2400. Loss: 0.992313027381897. Accuracy: 37.17299578059072% Iteration: 2500. Loss: 0.9095526933670044. Accuracy: 35.822784810126585% Iteration: 2600. Loss: 0.9182242155075073. Accuracy: 35.527426160337555% Iteration: 2700. Loss: 1.0588282346725464. Accuracy: 26.11814345991561% Iteration: 2800. Loss: 2.2419352531433105. Accuracy: 26.666666666666668% Iteration: 2900. Loss: 0.968953013420105. Accuracy: 27.0042194092827% Iteration: 3000. Loss: 0.6233363151550293. Accuracy: 26.70886075949367% Iteration: 3100. Loss: 2.0487442016601562. Accuracy: 27.0042194092827% Iteration: 3200. Loss: 3.055790901184082. Accuracy: 28.185654008438817% Iteration: 3300. Loss: 0.8039845824241638. Accuracy: 29.535864978902953% Iteration: 3400. Loss: 1.7581875324249268. Accuracy: 27.21518987341772% Iteration: 3500. Loss: 1.3234251737594604. Accuracy: 30.379746835443036% Iteration: 3600. Loss: 3.1995558738708496. Accuracy: 28.860759493670887% Iteration: 3700. Loss: 1.6779189109802246. Accuracy: 26.160337552742615% Iteration: 3800. Loss: 1.1911900043487549. Accuracy: 34.472573839662445% Iteration: 3900. Loss: 1.6331024169921875. Accuracy: 33.50210970464135% Iteration: 4000. Loss: 2.6895594596862793. Accuracy: 29.873417721518987% Iteration: 4100. Loss: 1.2465745210647583. Accuracy: 33.83966244725738% Iteration: 4200. Loss: 0.8581326007843018. Accuracy: 29.282700421940927% Iteration: 4300. Loss: 1.0225093364715576. Accuracy: 29.662447257383967% Iteration: 4400. Loss: 1.6333779096603394. Accuracy: 26.62447257383966% Iteration: 4500. Loss: 0.9013774394989014. Accuracy: 27.426160337552744% Iteration: 4600. Loss: 2.1769144535064697. Accuracy: 26.751054852320674% Iteration: 4700. Loss: 3.364973306655884. Accuracy: 26.11814345991561% Iteration: 4800. Loss: 0.7916203141212463. Accuracy: 25.61181434599156% Iteration: 4900. Loss: 0.8202955722808838. Accuracy: 29.40928270042194% Iteration: 5000. Loss: 0.5903524160385132. Accuracy: 27.932489451476794% Iteration: 5100. Loss: 0.7406677603721619. Accuracy: 31.265822784810126% Iteration: 5200. Loss: 1.2723222970962524. Accuracy: 27.890295358649787% Iteration: 5300. Loss: 2.3024892807006836. Accuracy: 32.82700421940928% Iteration: 5400. Loss: 0.5428053140640259. Accuracy: 31.60337552742616% Iteration: 5500. Loss: 1.5064878463745117. Accuracy: 33.50210970464135% Iteration: 5600. Loss: 1.4997795820236206. Accuracy: 32.53164556962025% Iteration: 5700. Loss: 1.4332350492477417. Accuracy: 31.729957805907173% Iteration: 5800. Loss: 0.8079369068145752. Accuracy: 35.44303797468354% Iteration: 5900. Loss: 2.8776166439056396. Accuracy: 32.06751054852321% Iteration: 6000. Loss: 0.6509627103805542. Accuracy: 28.10126582278481% Iteration: 6100. Loss: 1.9700945615768433. Accuracy: 25.485232067510548% Iteration: 6200. Loss: 0.4918310344219208. Accuracy: 29.70464135021097% Iteration: 6300. Loss: 2.7772140502929688. Accuracy: 35.18987341772152% Iteration: 6400. Loss: 0.8555337190628052. Accuracy: 35.232067510548525% Iteration: 6500. Loss: 1.0478401184082031. Accuracy: 35.822784810126585% Iteration: 6600. Loss: 2.5759568214416504. Accuracy: 31.561181434599156% Iteration: 6700. Loss: 0.8114219903945923. Accuracy: 34.89451476793249% Iteration: 6800. Loss: 3.87605881690979. Accuracy: 33.08016877637131% Iteration: 6900. Loss: 1.1042516231536865. Accuracy: 34.85232067510549% Iteration: 7000. Loss: 3.5895676612854004. Accuracy: 31.772151898734176% Iteration: 7100. Loss: 1.9772005081176758. Accuracy: 34.345991561181435% Iteration: 7200. Loss: 1.0952376127243042. Accuracy: 35.61181434599156% Iteration: 7300. Loss: 1.5407906770706177. Accuracy: 35.14767932489451% Iteration: 7400. Loss: 1.9394440650939941. Accuracy: 33.924050632911396% Iteration: 7500. Loss: 1.1104719638824463. Accuracy: 34.30379746835443% Iteration: 7600. Loss: 1.1241114139556885. Accuracy: 29.91561181434599% Iteration: 7700. Loss: 3.3067495822906494. Accuracy: 32.53164556962025% Iteration: 7800. Loss: 2.8544211387634277. Accuracy: 31.645569620253166% Iteration: 7900. Loss: 0.8465015292167664. Accuracy: 34.55696202531646% Iteration: 8000. Loss: 0.9381197094917297. Accuracy: 35.86497890295359% Iteration: 8100. Loss: 1.0465776920318604. Accuracy: 35.27426160337553% Iteration: 8200. Loss: 2.228691816329956. Accuracy: 32.447257383966246% Iteration: 8300. Loss: 3.2617666721343994. Accuracy: 31.561181434599156% Iteration: 8400. Loss: 1.222245693206787. Accuracy: 30.548523206751057% Iteration: 8500. Loss: 0.9302880167961121. Accuracy: 35.780590717299575% Iteration: 8600. Loss: 0.43104666471481323. Accuracy: 32.70042194092827% Iteration: 8700. Loss: 3.023744583129883. Accuracy: 32.23628691983122% Iteration: 8800. Loss: 0.8267171382904053. Accuracy: 35.527426160337555% Iteration: 8900. Loss: 1.0274813175201416. Accuracy: 36.70886075949367% Iteration: 9000. Loss: 0.6004358530044556. Accuracy: 31.814345991561183% Iteration: 9100. Loss: 2.752762794494629. Accuracy: 32.742616033755276% Iteration: 9200. Loss: 1.7081143856048584. Accuracy: 33.67088607594937% Iteration: 9300. Loss: 1.1600185632705688. Accuracy: 32.151898734177216% Iteration: 9400. Loss: 1.2398430109024048. Accuracy: 32.869198312236286% EPOCH -- 2 Iteration: 0. Loss: 2.137118101119995. Accuracy: 34.0084388185654% Iteration: 100. Loss: 1.3867638111114502. Accuracy: 34.72573839662447% Iteration: 200. Loss: 2.0789647102355957. Accuracy: 32.95358649789029% Iteration: 300. Loss: 2.462944984436035. Accuracy: 33.628691983122366% Iteration: 400. Loss: 0.20586931705474854. Accuracy: 33.628691983122366% Iteration: 500. Loss: 0.9252246618270874. Accuracy: 35.61181434599156% Iteration: 600. Loss: 2.5626983642578125. Accuracy: 34.68354430379747% Iteration: 700. Loss: 1.8939013481140137. Accuracy: 32.78481012658228% Iteration: 800. Loss: 1.7825818061828613. Accuracy: 33.29113924050633% Iteration: 900. Loss: 1.307302713394165. Accuracy: 30.29535864978903% Iteration: 1000. Loss: 0.8118931651115417. Accuracy: 33.83966244725738% Iteration: 1100. Loss: 1.2020342350006104. Accuracy: 35.69620253164557% Iteration: 1200. Loss: 2.3106884956359863. Accuracy: 36.118143459915615%
--------------------------------------------------------------------------- KeyboardInterrupt Traceback (most recent call last) <ipython-input-102-dfa1cee8c594> in <module> 24 sent = sent.cuda() 25 label = label.cuda() ---> 26 output = model.forward(sent)[0] 27 _, predicted = torch.max(output.data, 1) 28 total += label.size(0) ~/.local/lib/python3.8/site-packages/pytorch_transformers/modeling_roberta.py in forward(self, input_ids, token_type_ids, attention_mask, labels, position_ids, head_mask) 316 def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None, 317 position_ids=None, head_mask=None): --> 318 outputs = self.roberta(input_ids, position_ids=position_ids, token_type_ids=token_type_ids, 319 attention_mask=attention_mask, head_mask=head_mask) 320 sequence_output = outputs[0] ~/.local/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs) 1049 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks 1050 or _global_forward_hooks or _global_forward_pre_hooks): -> 1051 return forward_call(*input, **kwargs) 1052 # Do not call functions when jit is used 1053 full_backward_hooks, non_full_backward_hooks = [], [] ~/.local/lib/python3.8/site-packages/pytorch_transformers/modeling_roberta.py in forward(self, input_ids, token_type_ids, attention_mask, position_ids, head_mask) 176 "This model requires special tokens in order to work. " 177 "Please specify add_special_tokens=True in your encoding.") --> 178 return super(RobertaModel, self).forward(input_ids, token_type_ids, attention_mask, position_ids, head_mask) 179 180 ~/.local/lib/python3.8/site-packages/pytorch_transformers/modeling_bert.py in forward(self, input_ids, token_type_ids, attention_mask, position_ids, head_mask) 706 707 embedding_output = self.embeddings(input_ids, position_ids=position_ids, token_type_ids=token_type_ids) --> 708 encoder_outputs = self.encoder(embedding_output, 709 extended_attention_mask, 710 head_mask=head_mask) ~/.local/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs) 1049 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks 1050 or _global_forward_hooks or _global_forward_pre_hooks): -> 1051 return forward_call(*input, **kwargs) 1052 # Do not call functions when jit is used 1053 full_backward_hooks, non_full_backward_hooks = [], [] ~/.local/lib/python3.8/site-packages/pytorch_transformers/modeling_bert.py in forward(self, hidden_states, attention_mask, head_mask) 429 all_hidden_states = all_hidden_states + (hidden_states,) 430 --> 431 layer_outputs = layer_module(hidden_states, attention_mask, head_mask[i]) 432 hidden_states = layer_outputs[0] 433 ~/.local/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs) 1049 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks 1050 or _global_forward_hooks or _global_forward_pre_hooks): -> 1051 return forward_call(*input, **kwargs) 1052 # Do not call functions when jit is used 1053 full_backward_hooks, non_full_backward_hooks = [], [] ~/.local/lib/python3.8/site-packages/pytorch_transformers/modeling_bert.py in forward(self, hidden_states, attention_mask, head_mask) 410 attention_output = attention_outputs[0] 411 intermediate_output = self.intermediate(attention_output) --> 412 layer_output = self.output(intermediate_output, attention_output) 413 outputs = (layer_output,) + attention_outputs[1:] # add attentions if we output them 414 return outputs ~/.local/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs) 1043 return result 1044 -> 1045 def _call_impl(self, *input, **kwargs): 1046 forward_call = (self._slow_forward if torch._C._get_tracing_state() else self.forward) 1047 # If we don't have any hooks, we want to skip the rest of the logic in KeyboardInterrupt: